Method To Diagnose and Measure Vascular Drainage Insufficiency in the Central Nervous System

ABSTRACT

Neurodegenerative diseases, such as multiple sclerosis, may be caused or aggravated by insufficient venous draining from the central nervous system. Functional MRI measures the surge of blood flow into localized regions of cerebral cortex in response to activation of those regions by performing visual, auditory or executive tasks. These fMRI measurements are based on blood-oxygen-level dependence. The resulting fMRI/BOLD data is converted to hemodynamic response data and analyzed to determine any abnormality in the hemodynamic response data. Vascular drainage insufficiency is identified in the presence of abnormal hemodynamic response data. Abnormal hemodynamic response data can be determined by a negative trough in a graph of the HDR data or by the duration, depth, or area of the negative trough.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/497,950, filed on Mar. 23, 2012 and issued as U.S. Pat. No. 8,571,634on Oct. 29, 2013, which is a national stage application under 35 U.S.C.371 of International Patent App. No. PCT/US2011/044708, filed on Jul.20, 2011, which claims priority to U.S. Provisional Patent App. No.61/367,059, filed on Jul. 23, 2010, all of which are hereby incorporatedherein by reference in their entireties.

BACKGROUND

1. Field of the Invention

The present invention is generally related to functional magneticresonance imaging and the hemodynamic response to cognitive stimuli andis more specifically related to diagnosing and measuring vasculardrainage insufficiency in the central nervous system using fMRI andBOLD.

2. Related Art

When the brain is active, it requires an increase in blood flow to thebrain cells in the active region. The increase in blood flow typicallyoccurs after a brief delay (e.g., 1-5 seconds) and usually peaks ataround 4-5 seconds. After the peak, the increased blood flow washes outand typically exhibits a negative trough before returning to a normalbaseline level. This process of increased blood flow and correspondingwashout is referred to as a hemodynamic response (“HDR”).

When the increased blood flow is delivered to the active region of thebrain, the brain cells use the oxygen and glucose in the blood.Consequently, the deoxygenated blood remaining in the veins isparamagnetic and can be successfully imaged using magnetic resonanceimaging. Imaging based on the magnetic contrast of deoxygenated blood isreferred to as blood-oxygen-level dependence (“BOLD”).

Functional magnetic resonance imaging (“fMRI”) is used to capturecomplete scans of the brain during the HDR process, which typicallytakes about 15 seconds overall. The result of fMRI is a series of scansof the subject over time that show what region of the brain was activeduring the HDR. A single scan includes a full set of slices that coverthe brain of the subject. Each slice is a separate image andcollectively the slices comprise a three dimensional image of the brainof the subject. Typically, a scan is taken every 1-4 seconds. In thisfashion, fMRI is used to identify the region of the brain that is activefor a particular cognitive task.

SUMMARY

Conventional wisdom with respect to HDR is that the response proceedsnearly identically in all subjects, specifically that there is anincrease in blood flow to a particular area responding to the increasein energy consumption by cells of that area and that the increased bloodflow later washes out of the area and results in a slight trough, calledthe venous undershoot.

However the inventor has recognized that HDR is not identical in allsubjects and more importantly that neurodegenerative diseases, such asmultiple sclerosis (“MS”), may be caused or aggravated by insufficientvenous draining from the central nervous system. Furthermore, theinventor has recognized that fMRI can be adapted to diagnose and measurevascular drainage insufficiency in the central nervous system.

Multiple sclerosis (“MS”) is an inflammatory demyelinating disease andthe causes of MS remain elusive and currently no cure exists for thiscondition. While it is widely considered to be of autoimmune nature,there is a renewed interest in the hypothesis that MS may be associatedwith impaired central nervous system venous drainage, for example,chronic cerebrospinal venous insufficiency (“CCSVI”) caused by stenosesin large extracerebral veins. Such insufficiency may have directconsequences for both hemodynamics and function of cerebral parenchyma.Functional MRI based on BOLD contrast reflects both neuronal populationresponses and hemodynamics and the inventor has recognized that it canbe used to assess changes in neuronal activity and hemodynamics due toMS.

In a group of MS patients, as compared to the control group, themagnitude of cognitive task-related BOLD signal modulation in graymatter was reduced in both the task-positive network and in thetask-negative default mode network (“DMN”) that is characteristicallysuppressed during task performance. Moreover, the HDR in sometask-positive network areas exhibit increased post-stimulus undershoot,consistent with the hypothesis of impaired venous blood clearance.Remarkably, angioplastic treatment of jugular veins increased activityand reduced the BOLD undershoot in some task-positive areas andrecovered activity in the DMN. Accordingly, HDR and BOLD can be used toidentify and track improvements in MS symptomatology.

Other features and advantages of the present invention will become morereadily apparent to those of ordinary skill in the art after reviewingthe following detailed description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of the present invention, both as to its structure andoperation, may be gleaned in part by study of the accompanying drawings,in which like reference numerals refer to like parts, and in which:

FIG. 1 is a block diagram illustrating an example of a task called TellTime according to an embodiment of the present invention;

FIG. 2 is a statistical parametrical map diagram illustrating examplefMRI data according to an embodiment of the present invention;

FIG. 3 is a graph diagram illustrating example HDR data according to anembodiment of the present invention;

FIG. 4 is a graph diagram illustrating example fMRI data graph of BOLDover time and showing the hemodynamic response in a multiple sclerosispatient and a control patient according to an embodiment of the presentinvention;

FIG. 5 is a graph diagram illustrating example fMRI data graph of BOLDover time and showing the hemodynamic response in a multiple sclerosispatient prior to angioplasty and after angioplasty according to anembodiment of the present invention;

FIG. 6 is a flow diagram illustrating an example fMRI process adapted todiagnose vascular drainage insufficiency in the cerebral cortexaccording to an embodiment of the present invention; and

FIG. 7 is a block diagram illustrating an example computer system thatmay be used in connection with various embodiments described herein.

DETAILED DESCRIPTION

Certain embodiments as disclosed herein provide for systems and methodsto diagnose vascular drainage insufficiency in the central nervoussystem. For example, one method as disclosed herein provides forconverting fMRI/BOLD obtained from a subject in response to a particularcognitive task into HDR data and then analyzing the HDR data todetermine an abnormal HDR and thereby identify a vascular drainageinsufficiency in the central nervous system based on the abnormal HDRresponse. Additionally, a negative trough in a graph of the HDR data canbe used to determine the abnormal HDR and the duration, depth or area ofthe negative trough can be used alone or in combination to determine theabnormal HDR.

After reading this description it will become apparent to one skilled inthe art how to implement the invention in various alternativeembodiments and alternative applications. However, although variousembodiments of the present invention will be described herein, it isunderstood that these embodiments are presented by way of example only,and not limitation. As such, this detailed description of variousalternative embodiments should not be construed to limit the scope orbreadth of the present invention as set forth in the appended claims.

MS is one of the most prevalent neurodegenerative disease diagnosed inpatients who are in the age range of 20 to 50 years. It is estimatedthat over 2 million people worldwide suffer form this condition.However, the etiology of this disease is presently unknown and thereexists no cure for it. While it was proposed as early as in 1937 that MSmight have a vascular etiology, presently the most popular theoryregarding its causes is autoimmune theory and modern treatment of MStargets the immune system (e.g. by means of interferon-β). Recently ahigh incidence of CCSVI reported in MS patients generated much attentionamong MS researchers and patients alike because CCSVI can be treatedusing a relatively simple procedure, an angioplasty treatment targetingstenoses primarily in large extracerebral veins (such as jugular andazygos veins). Advantageously, establishing a causal relationshipbetween MS and CSSVI may have dramatic consequences for understanding MSetiology and possibly help find a cure for it.

One of the hallmarks of MS is lesions containing demyelinated axons andclustering around venules and veins of CNS white matter. White matter(“WM”) demyelination that results in disconnection of axonsinterconnecting cortical (and subcortical) regions is thought to be theneural basis of cognitive impairments in MS.

Among most common cognitive impairments associated with MS are motordysfunction, mood disorders, memory and attention deficits, withinformation processing speed and memory disorders dominating inrelapsing-remitting MS (“rrMS”). Previous PET brain imaging studiesaddressing functional correlates of cognitive impairment in MS foundwidespread gray and white matter reduction in cerebral metabolic rate ofglucose and oxygen (“CMRglu, CMRO2”) that correlated with both the WMlesion load and impairment in executive control, attention processes andlong-term memory.

Subsequent MRI studies addressing the relationship between WM lesionload and fMRI responses during cognitive and motor tasks, foundcorrelations between the two measures in both the motor and episodicmemory system. Interestingly, BOLD activation associated with motortasks differed in both magnitude and distribution in MS patients ascompared to controls, which was interpreted as evidence for compensatoryreorganization of motor task-related cortical networks. Increased BOLDactivation in MS patients as compared to controls was also observed inprefrontal cortex during attention-demanding tasks, and episodic memoryretrieval. However, in light of earlier PET results that foundgeneralized cerebral metabolic rate reduction in MS patients and thefact that BOLD signal is a function of both cerebral metabolism rate andblood flow, this enhancement in BOLD responses needs to be interpretedcarefully, as it may be an outcome of, for example, reduced baselineneuronal activity.

Default mode network, thought to mediate intrinsic states such asself-referential processes, moral judgment and episodic future planningis also affected in MS patients. DMN is suppressed during cognitive taskperformance or other externally-oriented activity, and the degree of itssuppression correlates with cognitive performance. The functionalconnectivity among areas belonging to DMN (e.g., ventral medialprefrontal cortex, posterior cingulate cortex, inferior parietal lobule,hippocampal formation, anterior part of the inferior temporal sulcus) ispreserved during resting state. In patients with both primaryprogressive MS (“ppMS”) and secondary progressive MS (“spMS”) DMNactivity as measured by resting state correlations is substantiallydiminished in all cortical areas of DMN, with anterior cingulate cortex(“ACC”) affected more in spMS. The reduction in DMN activity correlatedwith measures of memory performance, consistent with recent reports thatDMN is involved in memory encoding and retrieval. In addition, reducedactivity in DMN of MS patients also correlated with diffusion MRImeasures of WM damage, such as mean diffusivity and fractionalanisotropy, thus further supporting the notion that functionalimpairments in MS are an outcome of the structural damage to WM.

Recently, the hypothesis that MS may be associated with impaired CNSvenous drainage was revisited and revealed a high incidence of CCSVI inMS patients. This finding is consistent with long acknowledgeddeficiency of cerebral blood flow (“CBF”) in MS, and raises a hypothesisthat at least some of the MS symptoms, including cognitive impairmentsand reduced neuronal responses as measured by BOLD fMRI, are a directconsequence of impaired cerebral venous blood clearance.

The inventor addressed this hypothesis by measuring BOLD responses in MSpatients performing a cognitive task before and after an angioplastictreatment of CCSVI. It was found that BOLD responses in task-positivecortical and subcortical regions as well as BOLD response suppression inDMN (task-negative regions) were much reduced in MS patients as comparedto controls. Furthermore, the shape of HDR functions differed fromcontrols in some cortical areas of MS patients, the most prominentfeature being an increased after-stimulus undershoot, which isconsistent with compromised clearance of venous blood from gray mattervenules.

Most importantly, BOLD responses after the angioplastic procedureincreased in some task-positive cortical areas, and task-relatedsuppression of DMN recovered to levels comparable to that of the controlgroup. Moreover, the procedure resulted in a trend towards a smallernegative trough area of the after-stimulus undershoot. These changes inBOLD response magnitude and HDR shape brought about by angioplasty arein the direction towards the values observed in the control group. Themost prominent among those changes was the recovery of the DMN activityafter the procedure. The exciting conclusion is that the angioplasticintervention in MS patients suffering from CCSVI normalizes corticalBOLD responses in these patients and is likely to alleviate MS symptoms.Although the angioplasty effect on BOLD responses may have both neuronaland vascular components that the BOLD signal cannot disentangle,simultaneous BOLD and CBF measurements by means of arterial spinlabeling (“ASL”) will disentangle these contributions.

Certain aspects of the invention will now be described in the context ofan example test that was performed in which the functional andstructural MRI scans from twenty MS/CCSVI patients (14 females) wereanalyzed as a part of diagnostic evaluation for the angioplasty. Fifteennormal control subjects (9 females) were also scanned using identicalprotocols. MS patients were scanned before an angioplastic procedure andone to two days after the procedure. Thirteen of those subjects had aconfirmed relapsing-remitting type (“rrMS”) and two were diagnosed withprimary progressive MS (“ppMS”).

FIG. 1 is a block diagram illustrating an example Tell Time taskaccording to an embodiment of the present invention. In the Tell Timetask, subjects listened to spoken time statements (hours and minutes)and simultaneously viewed two clock faces presented on both sides of thefixation cross. The subject's task is to indicate the clock that showedthe time that differed from the spoken one. A single three second tasktrial is followed by a thirty-eight second fixation interval followed bya block of eight trials and a sixty-one second fixation interval.Alternative tasks or protocols can also be administered to the patient,for example finger tapping tasks and language tasks. Additional tasks orprotocols or other alternative tasks or protocols may also beadministered to the patient as will be understood by those skilled inthe art.

The cognitive task is designed to activate a maximum number of corticaland subcortical regions while still being intuitive to an untrainedperson. While in the scanner, subjects hear a spoken time of day viaMR-compatible headphones (e.g., Avotec Inc., Sturat, Fla.) andsimultaneously see a display screen with a fixation cross presentedthereon with two clock faces, one on each side of the display screen asshown in FIG. 1. The Tell Time task is presented during each trial via aprojector (e.g., 5200 lumens, NEC NP4000, Tokyo, Japan) using apresentation program (e.g., such as that provided by NeurobehavioralSystems, Inc., Albany, Calif.). In one embodiment, the distance from thesubject's eyes to the screen is about 36 inches and each clock facesubtended 6.4 degrees of visual angle.

In the Tell Time task, one of the clocks shows a time that is the sameas the spoken time, while the other clock shows a time that is differentfrom the spoken time. The subject is instructed to press a button on anMRI-compatible response box (e.g., FORP, Current Designs, Philadelphia,Pa.) indicating which side of the fixation cross has the clock showingthe time is different from the spoken time. In one embodiment, eachtrial is repeated seven times as follows: first, a single task ispresented followed by a thirty-eight second fixation interval, then ablock of eight tasks is presented followed by sixty one seconds offixation. The first trial is presented after forty-four seconds (22 TRs)from the beginning of the EPI scan for the purpose of establishing abaseline signal. This temporal pattern is aimed at revealing hemodynamicresponses (HDRs) associated with a single vs. blocked tasks.

During the example test mentioned above, MRI scans of control subjectsand MS patients were acquired using a Siemens Trio 3T system. MSpatients were scanned before and after undergoing the angioplasticprocedure. Structural scans at the resolution of 1×1×1 mm voxels wereacquired using MP-RAGE protocol (TR/TE/TI=1900/2.26/900 ms, flip angle=9deg). Functional T2*-weighted images were acquired using gradient echoEPI sequence with parameters TR/TE=2000/25 ms, flip angle=90 degrees, 36slices of thickness=3 mm, in-plane resolution: 3.75×3.75 mm2, spacingbetween slices=4 mm. The number of repititions was 463 resulting in scanduration of 15.37 minutes.

Additionally, as part of the example test MRI image preprocessing andprocessing steps were performed with a custom-developed analysis toolboxfor Matlab (MathWorks, Natic, Mass.) with integrated calls to a subsetof functions from the AFNI (Cox, 1996), FSL (Smith et al., 2004) andmrVista (mrVista, 2011) software packages. Structural scans wereprocessed by computationally removing skull and aligning to theTalairach-Tourneaux space (TT-space). Functional scans were smoothedwith a five mm smoothing kernel, resampled at four mm resolution, headmotion was corrected by aligning volumes at each time point to areference volume and the resulting volumes were stripped of skull,aligned to structural scans and mapped to the TT-space. A referencefunction, created by convolving task occurrence times with a standardgamma distribution function, was then used for calculation offirst-level individual t-maps by means of the generalized least squaresfitting procedure as implemented in 3dREMLfit. Head motion parameterswere projected out from fMRI time series at this step. At the secondstep, group statistical maps were calculated using a mixed-effectsprocedure implemented in 3dMEMA. Mixed-effects t-maps were generated foreach experimental group (controls, pre-angio and post-angio) as well asfor group differences (control vs. pre-angio and pre- vs. post-angio).Group t-maps for the control group were thresholded at p<0.05 (|t|>2.1)and multiple comparison-corrected significantly active voxel clusterswere then determined using permutation analysis (p<0.01).

For evaluation of the extent of active regions in terms of the number ofactive voxels and time course of hemodynamic responses masks were firstcreated for regions of interest (“ROI”) in the following way: activevoxel clusters determined in pooled group analysis of control subjectsand MS patients, as described above, were intersected with anatomy basedregions derived from the Talairach-Tourneaux atlas thus resulting inanatomically constrained cortical and subcortical ROI masks for bothtask-positive areas (positive response magnitudes) and task-negativeareas that coincided with the DMN negative response magnitudes. Thetask-positive ROIs and corresponding Brodmann's areas (for corticalregions) are listed in Table 1.

TABLE I Voxel counts in anatomical regions with task-positive ortask-negative active voxel clusters. Control Pre-angio Post-angio groupgroup group Brodman's Voxel # ± Voxel # ± Voxel # ± Anatomical Regionareas stderr stderr stderr Task-positive areas All task-positive areasn/a *27261 ± 5636  **15738 ± 7807   22479 ± 6629 Occipital poles 17, 18,19 5042 ± 727 4370 ± 1792  4043 ± 1574 Fusiform, Parahippocampal gyri20, 36, 37 1278 ± 301 1180 ± 565  1237 ± 351 (FFG, PHG) Superiortemporal cortex (STC) 21, 22, 41 3601 ± 700 3993 ± 1543 3354 ± 862Superior and inferior parietal lobules  7, 40 2757 ± 706 **1559 ± 858  2230 ± 728 (SPL, IPL), precuneus Precentral gyms (PCG)  6, 4 2757 ± 7061559 ± 858  2230 ± 728 Cingulate gyrus (CG) 24, 32 307 ± 60 378 ± 165 414 ± 112 Dorso-lateral prefrontal cortex 9, 45, 46  907 ± 212 1418 ±635  1335 ± 358 (DLPFC) Thalamus n/a 1356 ± 179 819 ± 403  765 ± 414Basal ganglia (BG) n/a  467 ± 144 561 ± 328  414 ± 213 Cerebellum n/a 5059 ± 1090 3331 ± 1848 3311 ± 990 Task-negative areas Alltask-negative areas (DMN) n/a 1215 ± 361 **573 ± 260  ***1038 ± 231  Anterior cingulate cortex (ACC), 24, 32, 10 1087 ± 336 407 ± 225 ***724± 164  medial frontal gyrus (MFG) Superior frontal gyrus (SFG)  8, 9  62± 16 134 ± 92  111 ± 56 Posterior cingulate cortex (PCC), 23, 30, 31  20± 10 28 ± 14  31 ± 11 precuneus Middle temporal gyrus (MTG), 7, 18, 39120 ± 41 **12 ± 4   100 ± 47 angular gyms (AG) Temporal poles (TP),anterior 21, 38  37 ± 14 95 ± 63  23 ± 14 superior and inferior temporalgyms (STG, ITG) *significant effect across the three groups (p < 0.05,Kruskal-Wallis test, nonparametric ANOVA). **mean of pre-angio groupsignificantly less than that of the control group (p < 0.05, Wilcoxontest) ***mean of pre-angio group significantly less than that of thepost-angio group (p < 0.05, Wilcoxon test) This procedure assuredminimum circularity in fMRI data analysis. For quantification oftask-related effects in different ROIs, the number of significantlyactivated (p < 0.01, corrected) or suppressed (p < 0.05, corrected)voxels were counted within each ROI of each subject and group mean andstandard error was calculated.

Next, for estimation of hemodynamic response function shapes, these ROIswere used to constrain significantly active voxels for extraction oftime series of individual scans. The specificity of BOLD responseincreases when weakly responding or low signal to noise ratio (“SNR”)voxels are eliminated from analysis by increasing the t-map threshold.The voxels with maximum response magnitude contained large drainingveins. For task-positive and task-negative ROIs different t-values wereused that yielded comparable BOLD response specificity. Hence, fortask-positive voxels t>6 was used and for the task-negative voxels t<−4was used. These values resulted in consistent detection of activation inboth task-positive and task-negative areas of MS patients. The timeseries data were smoothed in the temporal domain (e.g., Gaussian kernelof two second standard deviation) and averaged both voxel-wise andblock-wise and the peak value was normalized to one (1) for each subjectbefore calculating group averaged HDRs and standard errors of the mean.The significance of the post-stimulus undershoot was then evaluated(following the response to the block of eight trials) by running t-testscomparing undershoot trough magnitudes estimated as means within timewindows (see the shaded rightmost regions in FIG. 3). Undershootmagnitude outliers were then eliminated before the data was submittedfor statistical tests.

FIG. 2 is a statistical parametrical map diagram illustrating examplefMRI data according to an embodiment of the present invention.Statistical parametrical maps (t-values, threshold Thr>2.1) are shown inFIG. 2 for:

-   -   (A) the control group (Max=15, Min=−11);    -   (B) MS patient pre-angio group (Min=−6, Max=7);    -   (C) MS patient post-angio group (Min=−9, Max=8);    -   (D) control—pre-angio (Min=−7, Max=9);    -   (E) paired t-test map for post-angio—pre-angio group differences        (Min=−8, Max=4; note the color bar range differences); and    -   (F) Voxel counts in control (white bars), pre-angio (black) and        post-angio (grey) groups for task-positive (Task+),        task-negative (DMN), posterior parietal cortex (PPC) and medial        frontal gyrus (MFG) ROIs.

In the example test, both MS patient and control groups performed theTell Time task with very few errors (>95% correct rate) and theperformance levels did not differ between control and patient groups. Inthe control group the task evoked activation in an extensive set ofbrain areas (task-positive network), including visual (occipital,fusiform and parahippocampal cortex), auditory (posterior superiortemporal gyms and angular gyms), association and executive regions(parietal, temporal and frontal lobes) as well as subcortical structuresincluding basal ganglia, thalamus and cerebellum (shown by thered/orange voxels in FIG. 2A and Table 1). Cortical areas suppressedduring the task performance (task-negative network) included medialprefrontal, anterior and posterior cingulate cortex and cuneus (shown bythe blue voxels in FIG. 2A and Table 1) and thus coincided with thedefault mode network.

In the MS patient group, task performance evoked task-positiveactivation that was substantially reduced as compared to the controlgroup. Moreover, suppression in cortical areas of the DMN was nearlyabsent (FIGS. 2B and 2D). However, after the angioplastic procedureactivation in the task-positive network increased in some areas (e.g.,in PPC and occipital poles). Most remarkably, suppression in the DMNareas was completely recovered (FIGS. 2C and 2E).

FIG. 2F plots voxel counts for task-positive network (top row bar plots)and DMN (bottom row bar plots). While the active voxel count is muchreduced in pre-angio MS patients, the mean post-angio voxel count intask-positive network shows a tendency to increase and in DMN the numberof significantly suppressed voxels is nearly identical to that of thecontrol group. A full list of active voxel counts in each ROI for allthree groups is presented in Table 1.

FIG. 3 is a graph diagram illustrating example HDR data according to anembodiment of the present invention. The HDR data is calculated fromfMRI data obtained from scans of the patient during and afterperformance of the Tell Time task. FIG. 3 illustrates group-averagednormalized HDR functions for control (black), pre-angio (red) andpost-angio (blue) groups.

-   -   (A) HDRs for the task-positive ROI in the superior temporal        cortex (STC) ROI (control vs. pre-angio t-test, p<0.05,        one-sided; pre-angio vs. post-angio t-test, p<0.05, one sided).    -   (B) HDRs for task-negative (DMN) ROI in the MTG-AG. Note that in        the top row panels (A,B) undershoots of pre-angio group HDRs        differ statistically significantly from both control and post        angio groups. Remarkably, HDRs of control and post-angio patient        groups are nearly identical.    -   (C) HDRs for task-positive ROI in FFG-PHC. In this case both        pre- and post-agio HDRs are nearly identical and there is a        trend towards prolonged HDR undershoot as compared to the        control group HDR.    -   (D) HDRs for the thalamic ROI (pre- vs. post-angio t-test,        p=0.07). Error bars are standard error of the mean. Short        vertical tics on the horizontal zero-line represent times of        task onset. Grey regions indicate time intervals for HDR        normalization (left) and estimation of undershoot magnitude        (right).

HDRs from four ROIs that exhibited substantial time course differencesacross the three groups are shown in FIG. 3. Hemodynamic responses in aminority of the fifteen ROIs (Table 1) of MS patients before angioplastydiffered from those of controls, with the most salient and consistentdifference occurring in the magnitude (e.g., FIG. 3A) or the duration(FIGS. 3C and 3D) of the post-stimulus undershoot.

Importantly, HDRs in the task-positive ROIs of the post-angio patientgroup had a tendency to match the time course close to that observed incontrol subjects (FIGS. 3A and 3D). But see also FIG. 3C where theduration of undershoots of pre- and post-angio HDRs are identical anddiffer from that of the control group. HDRs of task-negative ROIs alsohad a tendency to assume time courses similar to those of the controlgroup (FIG. 3B).

In view of the test example data, the observed differences in BOLDactivation patterns and time courses in MS vs. control groups suggests asubstantial reorganization in neurovascular activity in cortical andsubcortical grey matter in MS patients, consistent with previous studiesthat found generalized reduction in cerebral metabolic rate and cerebralperfusion. These previous studies also reported moderate correlationsbetween WM lesion load and reduction in metabolism and perfusion.Furthermore, the total lesional area was found to be larger in MSpatients with impaired cognition as compared to those with unimpaired.Such results have been interpreted as evidence that cognitiveimpairments and associated reduction in cerebral metabolism/perfusionare an outcome of the structural damage to WM including lesions andnormally appearing white matter (“NAWM”).

However, in the test example, the recovery after angioplasty of BOLDactivity in several cortical and subcortical regions, most dramaticallyin cortical areas of DMN, occurred within one to two days, which isinsufficient for WM repair through remyelination. Altered HDR timecourses in certain grey matter (“GM”) ROIs of MS patients and HDRnormalization after venous angioplasty provides evidence thatobstruction of flow and/or refluxes in extracranial veins affects thehemodynamics in the microvasculature of cerebral parenchyma. Thissuggests that alteration in BOLD responses in MS patients can be atleast partially attributed to factors associated with CCSVI and not tofactors associated with WM demyelination or related structuraldegradation.

Several previous fMRI studies addressed brain activation duringcognitive and motor tasks and reported increased BOLD responses in MSpatients. These studies employed tasks that required substantialcognitive or motor effort on the part of subjects. It is likely that inthese studies patients with cognitive or motor impairments found thosetasks more demanding than controls, as task difficulty control was notperformed in the prior studies. Levels of task difficulty are known tocorrelate with activity in fronto-parietal cortical network involved inexecutive function and memory/attention tasks. Thus, it is likely thatstronger activation in MS patients was evoked due to the compensatoryeffort required by MS patients. Moreover, increased BOLD responses canalso occur as a result of decreased baseline CBF. Thus, decreased CBF inMS patients likely also accentuated increases in BOLD responses.

The Tell Time task, by contrast, is a variation of a familiar task(time-telling done everyday), and was performed with a very low errorrate by both control and MS subjects.

DMN suppression recovery by angioplasty has important implications forcognition in MS. Impairments in DMN activity are common in psychiatricand neurological pathology: DMN activity deficits have been reported inclinical depression patients, ADHD patients, schizophrenia andAlzheimer's disease. While DMN is primarily considered to be involved inself-referential, or internal information processing, recent studiesimplicate DMN in episodic memory function: Recent correlation analysisof the resting state fMRI time courses have revealed strong functionalconnectivity between hippocampus and DMN areas and showed that parietalregions of DMN (AG, MTG, TPJ) are involved in episodic memory recall.

Episodic memory impairment is among the most common cognitiveimpairments in MS. Previous studies of the resting state correlations inthe DMN network areas found a reduced activation in MS, which isconsistent with the text example results. Thus, recovery of DMNmodulation (task-related suppression) after angioplastic procedureimproves episodic memory in MS patients and alleviates other cognitivedisorders associated with impairment of DMN function.

BOLD signal values depend on deoxyhemoglobin concentration in a voxeland thus BOLD responses are a complex function of baseline values ofCBF, CBV and CMRO2 and also of changes of these values due to neuronalactivity. For example, on one hand, an increase in CBF results inincreased BOLD signal. On the other hand an increase in CMRO2 results ina decrease in BOLD signal. Thus, in light of previously reportedreductions in both CBF and CMR in MS patients, the BOLD signal alonecannot be used for definitive determination of the mechanisms of alteredBOLD responses in MS. Furthermore, BOLD response reduction intask-positive areas reflects not only a decrease in neuronal populationresponses, but also changes in neurovascular coupling (e.g., reductionin CBF response, increase in CMR response relative to reduced baselineCMR, etc.). Therefore, disentangling vascular and metaboliccontributions to the BOLD signal can be accomplished using techniquessuch as arterial spin labeling (“ASL”).

In some areas of MS patients, enhanced HDR undershoot was observed inthe text example. The enhanced HDR undershoot returned to the shapeidentical to that of the control group after the angioplasty. TheBalloon Model assumed herein, postulates that the volume of the venousblood in the venules increases transiently in response to increased CBF,and returns back to baseline with a delay thus resulting inpost-stimulus undershoot. According to this model, the BOLDpost-stimulus undershoot reflects dynamics of clearance of venous blood,therefore, extended undershoot in MS patients according to this modelcorresponds to prolonged clearance of venous blood from the capillarybed.

FIG. 4 is a graph diagram illustrating example fMRI data graph of BOLDover time and showing the hemodynamic response in a multiple sclerosispatient and a control patient according to an embodiment of the presentinvention. In the illustrated embodiment, the HDR graph for an MSsubject is contrasted with the HDR graph for a control subject. Asshown, the peak for the MS subject is higher than the peak for thecontrol subject. Additionally, the negative trough for the MS subject islower than the negative trough for the control subject.

FIG. 5 is a graph diagram illustrating example fMRI data graph of BOLDover time and showing the hemodynamic response in a multiple sclerosispatient prior to angioplasty and after angioplasty according to anembodiment of the present invention. In the illustrated embodiment, thepre-angioplasty graphs represent four different regions of the subject'sbrain, namely prefrontal, precentral, parietal and visual. In theillustrated embodiment, each region demonstrates a similar HDRdetermined based on the fMRI BOLD measurements. Advantageously,examination and analysis of the HDR from different regions of the brainmay be suggestive of the location of vascular drainage insufficiency.

In the illustrated embodiment, each graph represents a single task (thefirst peak) followed by a block of tasks (second, prolonged peak). Inthe pre-angioplasty graphs, there is a deep trough after the single taskand a deep and prolonged trough after the block task. In the postangioplasty graphs, the negative troughs during washout aresignificantly improved.

In one embodiment, a specifically designed cognitive task sequence isused to elicit the desired fMRI BOLD data that can be analyzed togenerate the HDR graph. For example, the cognitive task sequenceincludes a single task followed by a block task (multiple tasks). Thesetasks advantageously may involve executive and other brain functions asdesired.

FIG. 6 is a flow diagram illustrating an example fMRI process adapted todiagnose vascular drainage insufficiency in the central nervous systemaccording to an embodiment of the present invention. In a broad sense,the illustrated embodiment of the present invention analyzes fMRI BOLDdata and generates HDR data to measure and diagnose vascular drainageinsufficiency in the central nervous system. For example, the amount andduration of venous delay in multiple cerebral cortex regions in responseto a cognitive task are measured and analyzed to diagnose vasculardrainage insufficiency in the central nervous system.

Initially, in step 100 of the illustrated embodiment, a subject (e.g.,MS patient or control subject) is placed in an MR scanner having meansto deliver aural and visual information to the subject. For example, thesubject may be wearing headphones and positioned to observe a displaymonitor. In this way, the subject can be aurally and visually engagedduring scanning. The subject is also holding response boxes, one in eachhand, so the subject can interact with aural and visual stimulation.

Next, in step 150 a standard structure T1-weighted MRI scan of the brainis taken to establish a baseline for the individual subject. Inalternative embodiments, the structural MR scan may be performed beforeor after the functional MR scan.

Then in step 200 a visual, auditory and cognitive task is presented tothe subject through the headphones and the display monitor. For example,the Tell Time task is presented to the subject. Responses from thesubject are received via the response boxes that are held in thesubject's hands. For example, the subject holds a first response box inthe left hand and holds a second response box in the right hand.

Next in step 250 a T2-weighted scan of the brain is performed to captureimages at approximately two second intervals. The scan images reflectthe HDR in multiple brain regions and the resulting images includevoxels (volumes of brain tissue, typically in the 1-9 mm cubed range).In step 300, the changes in the local magnetic field are measured invoxels as the subject performs the task. These changes in the localmagnetic field signal intensity are then plotted over time to create anHDR graph as shown in step 350. For example, the positive and negativeamplitudes and time course of the HDR is calculated based on the fMRIBOLD data that is captured by the MR scanner.

Finally, in step 400 the HDR graph is analyzed to determine if the HDRresponse is abnormal and vascular drainage insufficiency in the centralnervous system can be identified based on the abnormal HDR response.This can be achieved by comparing the HDR data to a database of normalvalues to identify abnormal HDR. A pattern significantly different fromnormal may indicate pathology in the flow of blood within the cerebralcortex and a prolonged or more negative trough may indicate a vasculardrainage insufficiency in the central nervous system. Components of thecentral nervous system that can be diagnosed include the cerebralcortex, sub-cortical white matter, thalamus, basal ganglia, brainstem,and the spinal cord.

Additionally, when the HDR data is analyzed, for example as a graph, anabnormal HDR response can be identified by a negative trough in the HDRdata. In addition, abnormal HDR can be identified by the duration of thenegative trough, the depth of the negative trough, or the area of thenegative trough in the HDR data.

In the illustrated embodiment, the analysis of the HDR is automated andincorporated into a computer that controls the scanner. Alternatively,the HDR analysis can be incorporated into a separate computer that hasaccess to the fMRI BOLD data so that the HDR data can be generated andanalyzed and compared.

As will be understood by those skilled in the art, structural andfunctional MR scanning is performed in the typical manner. The resultingimages can be stored in accordance with the digital imaging andcommunications in medicine (“DICOM”) standard and can also be exportedfrom the scanner to a computer for the analysis. As previouslydescribed, the analysis may also be performed by the MR scanner device.As will be understood, open-source, propriety or ad hoc software may beemployed to facilitate the image analysis.

In the example embodiment, a number of brain regions may be selected foranalysis and the HDR calculated for each region. Patterns of HDR can beanalyzed for individual regions or in combination and compared to thedatabase in the same single or collective fashion to identify abnormalpatterns. Advantageously, evidence of prolonged or deep venousundershoot are indicative of cerebral venous insufficiency.

In one embodiment, the system for presenting visual, auditory andexecutive tasks to a patient or research subject may be packaged into acombination of hardware and software, for example hardware and softwarethat are incorporated into an MR apparatus. The software for analyzingthe HDR data may also be incorporated into an MRI scanner device or itmay be resident on a separate computer device that has access to thefMRI BOLD data that is generated by the MR scanner.

FIG. 7 is a block diagram illustrating an example computer system 550that may be used in connection with various embodiments describedherein. For example, the computer system 550 may be used in conjunctionwith a functional magnetic resonance imaging machine or an fMRI analysisstation. However, other computer systems and/or architectures may beused, as will be clear to those skilled in the art.

The computer system 550 preferably includes one or more processors, suchas processor 552. Additional processors may be provided, such as anauxiliary processor to manage input/output, an auxiliary processor toperform floating point mathematical operations, a special-purposemicroprocessor having an architecture suitable for fast execution ofsignal processing algorithms (e.g., digital signal processor), a slaveprocessor subordinate to the main processing system (e.g., back-endprocessor), an additional microprocessor or controller for dual ormultiple processor systems, or a coprocessor. Such auxiliary processorsmay be discrete processors or may be integrated with the processor 552.

The processor 552 is preferably connected to a communication bus 554.The communication bus 554 may include a data channel for facilitatinginformation transfer between storage and other peripheral components ofthe computer system 550. The communication bus 554 further may provide aset of signals used for communication with the processor 552, includinga data bus, address bus, and control bus (not shown). The communicationbus 554 may comprise any standard or non-standard bus architecture suchas, for example, bus architectures compliant with industry standardarchitecture (“ISA”), extended industry standard architecture (“EISA”),Micro Channel Architecture (“MCA”), peripheral component interconnect(“PCI”) local bus, or standards promulgated by the Institute ofElectrical and Electronics Engineers (“IEEE”) including IEEE 488general-purpose interface bus (“GPIB”), IEEE 696/S-100, and the like.

Computer system 550 preferably includes a main memory 556 and may alsoinclude a secondary memory 558. The main memory 556 provides storage ofinstructions and data for programs executing on the processor 552. Themain memory 556 is typically semiconductor-based memory such as dynamicrandom access memory (“DRAM”) and/or static random access memory(“SRAM”). Other semiconductor-based memory types include, for example,synchronous dynamic random access memory (“SDRAM”), Rambus dynamicrandom access memory (“RDRAM”), ferroelectric random access memory(“FRAM”), and the like, including read only memory (“ROM”).

The secondary memory 558 may optionally include a hard disk drive 560and/or a removable storage drive 562, for example a floppy disk drive, amagnetic tape drive, a compact disc (“CD”) drive, a digital versatiledisc (“DVD”) drive, etc. The removable storage drive 562 reads fromand/or writes to a removable storage medium 564 in a well-known mannerRemovable storage medium 564 may be, for example, a floppy disk,magnetic tape, CD, DVD, etc.

The removable storage medium 564 is preferably a computer readablemedium having stored thereon computer executable code (i.e., software)and/or data. The computer software or data stored on the removablestorage medium 564 is read into the computer system 550 as electricalcommunication signals 578.

In alternative embodiments, secondary memory 558 may include othersimilar means for allowing computer programs or other data orinstructions to be loaded into the computer system 550. Such means mayinclude, for example, an external storage medium 572 and an interface570. Examples of external storage medium 572 may include an externalhard disk drive or an external optical drive, or and externalmagneto-optical drive.

Other examples of secondary memory 558 may include semiconductor-basedmemory such as programmable read-only memory (“PROM”), erasableprogrammable read-only memory (“EPROM”), electrically erasable read-onlymemory (“EEPROM”), or flash memory (block oriented memory similar toEEPROM). Also included are any other removable storage units 572 andinterfaces 570, which allow software and data to be transferred from theremovable storage unit 572 to the computer system 550.

Computer system 550 may also include a communication interface 574. Thecommunication interface 574 allows software and data to be transferredbetween computer system 550 and external devices (e.g. printers),networks, or information sources. For example, computer software orexecutable code may be transferred to computer system 550 from a networkserver via communication interface 574. Examples of communicationinterface 574 include a modem, a network interface card (“NIC”), acommunications port, a PCMCIA slot and card, an infrared interface, andan IEEE 1394 fire-wire, just to name a few.

Communication interface 574 preferably implements industry promulgatedprotocol standards, such as Ethernet IEEE 802 standards, Fiber Channel,digital subscriber line (“DSL”), asynchronous digital subscriber line(“ADSL”), frame relay, asynchronous transfer mode (“ATM”), integrateddigital services network (“ISDN”), personal communications services(“PCS”), transmission control protocol/Internet protocol (“TCP/IP”),serial line Internet protocol/point to point protocol (“SLIP/PPP”), andso on, but may also implement customized or non-standard interfaceprotocols as well.

Software and data transferred via communication interface 574 aregenerally in the form of electrical communication signals 578. Thesesignals 578 are preferably provided to communication interface 574 via acommunication channel 576. Communication channel 576 carries signals 578and can be implemented using a variety of wired or wirelesscommunication means including wire or cable, fiber optics, conventionalphone line, cellular phone link, wireless data communication link, radiofrequency (“RF”) link, or infrared link, just to name a few.

Computer executable code (i.e., computer programs or software) is storedin the main memory 556 and/or the secondary memory 558. Computerprograms can also be received via communication interface 574 and storedin the main memory 556 and/or the secondary memory 558. Such computerprograms, when executed, enable the computer system 550 to perform thevarious functions of the present invention as previously described.

In this description, the term “computer readable medium” is used torefer to any non-transitory computer readable storage media used toprovide computer executable code (e.g., software and computer programs)to the computer system 550. Examples of these media include main memory556, secondary memory 558 (including hard disk drive 560, removablestorage medium 564, and external storage medium 572), and any peripheraldevice communicatively coupled with communication interface 574(including a network information server or other network device). Thesenon-transitory computer readable mediums are means for providingexecutable code, programming instructions, and software to the computersystem 550.

In an embodiment that is implemented using software, the software may bestored on a computer readable medium and loaded into computer system 550by way of removable storage drive 562, interface 570, or communicationinterface 574. In such an embodiment, the software is loaded into thecomputer system 550 in the form of electrical communication signals 578.The software, when executed by the processor 552, preferably causes theprocessor 552 to perform the inventive features and functions previouslydescribed herein.

Various embodiments may also be implemented primarily in hardware using,for example, components such as application specific integrated circuits(“ASICs”), or field programmable gate arrays (“FPGAs”). Implementationof a hardware state machine capable of performing the functionsdescribed herein will also be apparent to those skilled in the relevantart. Various embodiments may also be implemented using a combination ofboth hardware and software.

Furthermore, those of skill in the art will appreciate that the variousillustrative logical blocks, modules, circuits, and method stepsdescribed in connection with the above described figures and theembodiments disclosed herein can often be implemented as electronichardware, computer software, or combinations of both. To clearlyillustrate this interchangeability of hardware and software, variousillustrative components, blocks, modules, circuits, and steps have beendescribed above generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled persons can implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the invention. In addition, the grouping of functions within amodule, block, circuit or step is for ease of description. Specificfunctions or steps can be moved from one module, block or circuit toanother without departing from the invention.

Moreover, the various illustrative logical blocks, modules, and methodsdescribed in connection with the embodiments disclosed herein can beimplemented or performed with a general purpose processor, a digitalsignal processor (“DSP”), an ASIC, FPGA or other programmable logicdevice, discrete gate or transistor logic, discrete hardware components,or any combination thereof designed to perform the functions describedherein. A general-purpose processor can be a microprocessor, but in thealternative, the processor can be any processor, controller,microcontroller, or state machine. A processor can also be implementedas a combination of computing devices, for example, a combination of aDSP and a microprocessor, a plurality of microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration.

Additionally, the steps of a method or algorithm described in connectionwith the embodiments disclosed herein can be embodied directly inhardware, in a software module executed by a processor, or in acombination of the two. A software module can reside in RAM memory,flash memory, ROM memory, EPROM memory, EEPROM memory, registers, harddisk, a removable disk, a CD-ROM, or any other form of storage mediumincluding a network storage medium. An exemplary storage medium can becoupled to the processor such the processor can read information from,and write information to, the storage medium. In the alternative, thestorage medium can be integral to the processor. The processor and thestorage medium can also reside in an ASIC.

The above description of the disclosed embodiments is provided to enableany person skilled in the art to make or use the invention. Variousmodifications to these embodiments will be readily apparent to thoseskilled in the art, and the generic principles described herein can beapplied to other embodiments without departing from the spirit or scopeof the invention. Thus, it is to be understood that the description anddrawings presented herein represent a presently preferred embodiment ofthe invention and are therefore representative of the subject matterwhich is broadly contemplated by the present invention. It is furtherunderstood that the scope of the present invention fully encompassesother embodiments that may become obvious to those skilled in the artand that the scope of the present invention is accordingly not limited.

What is claimed is:
 1. A system for identifying abnormal hemodynamicresponses, the system comprising: at least one hardware processor; andone or more modules configured to, when executed by the at least onehardware processor, obtain a baseline blood-oxygen-level-dependence(BOLD) signal value, obtain hemodynamic response data comprising a timeseries, wherein the time series comprises BOLD signal values over a timeperiod for the one or more regions of interest of a subject's brain,identify a negative trough in the hemodynamic response data, wherein thenegative trough comprises a portion of the time period during which theBOLD signal values of the time series are less than the baseline BOLDsignal value, and determine whether the hemodynamic response datarepresents an abnormal hemodynamic response based on the identifiednegative trough.
 2. The system of claim 1, wherein the one or moremodules are further configured to obtain functional magnetic resonanceimaging (“fMRI”) data over the time period for the one or more regionsof interest of the subject's brain, and wherein obtaining thehemodynamic response data comprises generating the hemodynamic responsedata based on the fMRI data.
 3. The system of claim 2, wherein the oneor more modules are further configured to obtain structural magneticresonance imaging (MRI) data for the one or more regions of interest ofthe subject's brain, and wherein obtaining the baseline BOLD signalvalue comprises generating the baseline BOLD signal value based on thestructural MRI data.
 4. The system of claim 2, wherein the fMRI datacomprises images of the subject's brain captured while the subjectperformed a single task, followed by a first time interval, followed bya block of multiple tasks, followed by a second time interval.
 5. Thesystem of claim 4, wherein the negative trough comprises at least aportion of the second time interval during which the BOLD signal valuesof the time series are less than the baseline BOLD signal value.
 6. Thesystem of claim 4, wherein the single task and the multiple tasks eachcomprise the same task, and wherein the task comprises providing one oftwo possible responses.
 7. The system of claim 1, wherein thedetermination of whether the hemodynamic response data represents anabnormal hemodynamic response is based, at least in part, on a durationof the identified negative trough.
 8. The system of claim 1, wherein thedetermination of whether the hemodynamic response data represents anabnormal hemodynamic response is based, at least in part, on a depth ofthe identified negative trough.
 9. The system of claim 1, wherein thedetermination of whether the hemodynamic response data represents anabnormal hemodynamic response is based, at least in part, on an area ofthe identified negative trough.
 10. A method for identifying abnormalhemodynamic responses, the method comprising using at least one hardwareprocessor to: obtain a baseline blood-oxygen-level-dependence (BOLD)signal value; obtain hemodynamic response data comprising a time series,wherein the time series comprises BOLD signal values over a time periodfor the one or more regions of interest of a subject's brain; identify anegative trough in the hemodynamic response data, wherein the negativetrough comprises a portion of the time period during which the BOLDsignal values of the time series are less than the baseline BOLD signalvalue; and determine whether the hemodynamic response data represents anabnormal hemodynamic response based on the identified negative trough.11. The method of claim 10, further comprising obtaining functionalmagnetic resonance imaging (“fMRI”) data over the time period for theone or more regions of interest of the subject's brain, and whereinobtaining the hemodynamic response data comprises generating thehemodynamic response data based on the fMRI data.
 12. The method ofclaim 11, further comprising obtaining structural magnetic resonanceimaging (MRI) data for the one or more regions of interest of thesubject's brain, and wherein obtaining the baseline BOLD signal valuecomprises generating the baseline BOLD signal value based on thestructural MRI data.
 13. The method of claim 11, wherein the fMRI datacomprises images of the subject's brain captured while the subjectperformed a single task, followed by a first time interval, followed bya block of multiple tasks, followed by a second time interval.
 14. Themethod of claim 13, wherein the negative trough comprises at least aportion of the second time interval during which the BOLD signal valuesof the time series are less than the baseline BOLD signal value.
 15. Themethod of claim 13, wherein the single task and the multiple tasks eachcomprise the same task, and wherein the task comprises providing one oftwo possible responses.
 16. The method of claim 10, wherein thedetermination of whether the hemodynamic response data represents anabnormal hemodynamic response is based, at least in part, on a durationof the identified negative trough.
 17. The method of claim 10, whereinthe determination of whether the hemodynamic response data represents anabnormal hemodynamic response is based, at least in part, on a depth ofthe identified negative trough.
 18. The method of claim 10, wherein thedetermination of whether the hemodynamic response data represents anabnormal hemodynamic response is based, at least in part, on an area ofthe identified negative trough.
 19. A non-transitory computer-readablemedium having one or more sequences of instructions stored thereon,wherein the one or more sequences of instructions are configured to,when executed by a processor, cause the processor to: obtain a baselineblood-oxygen-level-dependence (BOLD) signal value; obtain hemodynamicresponse data comprising a time series, wherein the time seriescomprises BOLD signal values over a time period for the one or moreregions of interest of a subject's brain; identify a negative trough inthe hemodynamic response data, wherein the negative trough comprises aportion of the time period during which the BOLD signal values of thetime series are less than the baseline BOLD signal value; and determinewhether the hemodynamic response data represents an abnormal hemodynamicresponse based on the identified negative trough.
 20. The non-transitorycomputer-readable medium of claim 19, wherein the determination ofwhether the hemodynamic response data represents an abnormal hemodynamicresponse is based, at least in part, on one or more of a duration of theidentified negative trough, a depth of the identified negative trough,and an area of the identified negative trough.